# Is variational inference faster than MCMC?

## Is variational inference faster than MCMC?

Variational Inference as an alternative to MCMC for parameter estimation and model selection. We find that variational inference is much faster than MCMC and nested sampling techniques for most of these problems while providing competitive results.

## Why do we use variational inference?

Variational Bayesian methods are primarily used for two purposes: To provide an analytical approximation to the posterior probability of the unobserved variables, in order to do statistical inference over these variables.

Is MCMC variational inference?

On one hand, with the variational distribution locating high posterior density regions, the Markov chain is optimized within the variational inference framework to efficiently target the posterior despite a small number of transitions. …

Why are MCMC methods important for Bayesian statistics?

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MCMC can be used in Bayesian inference in order to generate, directly from the “not normalised part” of the posterior, samples to work with instead of dealing with intractable computations.

### What is stochastic variational inference?

Stochastic variational inference makes it possible to approximate posterior distributions induced by large datasets quickly using stochastic optimization. The algorithm relies on the use of fully factorized variational distributions.

### What is variational inference in machine learning?

In variational inference, we avoid computing the marginal p(X). This partition function is usually nasty. Instead, we select some tractable families of distribution q to approximate p. For example, its expectation and the normalization factor can be computed directly from the distribution parameters.

What is MCMC in Bayesian statistics?

In statistics, Markov chain Monte Carlo (MCMC) methods comprise a class of algorithms for sampling from a probability distribution. By constructing a Markov chain that has the desired distribution as its equilibrium distribution, one can obtain a sample of the desired distribution by recording states from the chain.